Project: Concrete Strength Prediction¶
Import packages¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split as tts
import re
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import scale
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from mpl_toolkits.mplot3d import Axes3D
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn import tree
from sklearn.tree import DecisionTreeRegressor
from IPython.display import Image
from os import system
from sklearn.model_selection import cross_validate
from sklearn.model_selection import RandomizedSearchCV
from IPython.core.interactiveshell import InteractiveShell
# Typically would use inline
# but doing this for a 3d chart
# in the bivariate analysis section
%matplotlib inline
InteractiveShell.ast_node_interactivity = 'all'
plt.rc('figure', max_open_warning=0)
Set package options¶
In [2]:
sns.set(color_codes=True)
sns.set_style(style='darkgrid')
palette = 'Set2'
pd.set_option('display.max_columns', None)
Import dataset; one for raw data, one to edited/imputed data¶
In [3]:
raw_data = pd.read_csv('concrete.csv')
data = pd.read_csv('concrete.csv')
Methods for use elsewhere¶
In [4]:
figx = 10
figy = 8
def dist(col):
plt.figure(figsize=(figx,figy))
sns.distplot(col);
def hist(col):
plt.figure(figsize=(figx,figy))
plt.hist(col)
plt.axvline(col.mean(), color='y', linewidth=2, label='Mean')
plt.axvline(col.median(), color='g', linewidth=2, label='Median')
plt.legend();
def box(col):
plt.figure(figsize=(figx,figy))
sns.boxplot(col);
def print_summary(col):
dist(col)
hist(col)
box(col)
print(col.describe())
print('')
print('Unique values: ' + str(col.nunique()))
def replace_zeros(col, val):
data[col].replace(0, val, inplace=True)
def marginal_boxplot_margins(a, vertical=False, **kws):
if vertical:
sns.boxplot(y=a, palette='Accent', **kws)
else:
sns.boxplot(x=a, palette='Accent_r', **kws)
def marginal_boxplot(xcol, ycol, raw=True):
if raw:
g = sns.JointGrid(data=raw_data, x=xcol, y=ycol);
else:
g = sns.JointGrid(data=data, x=xcol, y=ycol);
g.plot_joint(sns.regplot, lowess=True, truncate=False, scatter_kws={'alpha':.2});
g.plot_marginals(marginal_boxplot_margins);
Generic data analysis¶
In [5]:
raw_data.head(10)
Out[5]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | 0.0 | 203.5 | 0.0 | 971.8 | 748.5 | 28 | 29.89 |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 |
| 2 | 250.0 | 0.0 | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 |
| 3 | 266.0 | 114.0 | 0.0 | 228.0 | 0.0 | 932.0 | 670.0 | 28 | 45.85 |
| 4 | 154.8 | 183.4 | 0.0 | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 |
| 5 | 255.0 | 0.0 | 0.0 | 192.0 | 0.0 | 889.8 | 945.0 | 90 | 21.86 |
| 6 | 166.8 | 250.2 | 0.0 | 203.5 | 0.0 | 975.6 | 692.6 | 7 | 15.75 |
| 7 | 251.4 | 0.0 | 118.3 | 188.5 | 6.4 | 1028.4 | 757.7 | 56 | 36.64 |
| 8 | 296.0 | 0.0 | 0.0 | 192.0 | 0.0 | 1085.0 | 765.0 | 28 | 21.65 |
| 9 | 155.0 | 184.0 | 143.0 | 194.0 | 9.0 | 880.0 | 699.0 | 28 | 28.99 |
In [6]:
raw_data.shape
Out[6]:
(1030, 9)
In [7]:
raw_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1030 entries, 0 to 1029 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 cement 1030 non-null float64 1 slag 1030 non-null float64 2 ash 1030 non-null float64 3 water 1030 non-null float64 4 superplastic 1030 non-null float64 5 coarseagg 1030 non-null float64 6 fineagg 1030 non-null float64 7 age 1030 non-null int64 8 strength 1030 non-null float64 dtypes: float64(8), int64(1) memory usage: 72.5 KB
In [8]:
raw_data.describe()
Out[8]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | |
|---|---|---|---|---|---|---|---|---|---|
| count | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 | 1030.000000 |
| mean | 281.167864 | 73.895825 | 54.188350 | 181.567282 | 6.204660 | 972.918932 | 773.580485 | 45.662136 | 35.817961 |
| std | 104.506364 | 86.279342 | 63.997004 | 21.354219 | 5.973841 | 77.753954 | 80.175980 | 63.169912 | 16.705742 |
| min | 102.000000 | 0.000000 | 0.000000 | 121.800000 | 0.000000 | 801.000000 | 594.000000 | 1.000000 | 2.330000 |
| 25% | 192.375000 | 0.000000 | 0.000000 | 164.900000 | 0.000000 | 932.000000 | 730.950000 | 7.000000 | 23.710000 |
| 50% | 272.900000 | 22.000000 | 0.000000 | 185.000000 | 6.400000 | 968.000000 | 779.500000 | 28.000000 | 34.445000 |
| 75% | 350.000000 | 142.950000 | 118.300000 | 192.000000 | 10.200000 | 1029.400000 | 824.000000 | 56.000000 | 46.135000 |
| max | 540.000000 | 359.400000 | 200.100000 | 247.000000 | 32.200000 | 1145.000000 | 992.600000 | 365.000000 | 82.600000 |
Univariate analysis¶
Cement (cement)¶
In [9]:
print_summary(raw_data['cement'])
count 1030.000000 mean 281.167864 std 104.506364 min 102.000000 25% 192.375000 50% 272.900000 75% 350.000000 max 540.000000 Name: cement, dtype: float64 Unique values: 278
Distribution of cement has a very slight right-hand skew.
Blast Furance Slag (slag)¶
In [10]:
print_summary(raw_data['slag'])
count 1030.000000 mean 73.895825 std 86.279342 min 0.000000 25% 0.000000 50% 22.000000 75% 142.950000 max 359.400000 Name: slag, dtype: float64 Unique values: 185
In [11]:
replace_zeros('slag', np.nan)
print_summary(data['slag'])
C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal keep = (tmp_a >= first_edge) C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal keep &= (tmp_a <= last_edge)
count 559.000000 mean 136.158676 std 72.351823 min 11.000000 25% 95.000000 50% 135.700000 75% 189.000000 max 359.400000 Name: slag, dtype: float64 Unique values: 184
Distribution of slag has a very large right hand skew.
Fly Ash (ash)¶
In [12]:
print_summary(raw_data['ash'])
count 1030.000000 mean 54.188350 std 63.997004 min 0.000000 25% 0.000000 50% 0.000000 75% 118.300000 max 200.100000 Name: ash, dtype: float64 Unique values: 156
Distribution of ash has a very strong right-hand skew, however this is largely due to most samples have no ash content.
What does it look like if we only look at samples containing some ash?
In [13]:
replace_zeros('ash', np.nan)
print_summary(data['ash'])
C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal keep = (tmp_a >= first_edge) C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal keep &= (tmp_a <= last_edge)
count 464.000000 mean 120.288793 std 33.675470 min 24.500000 25% 97.850000 50% 121.400000 75% 141.000000 max 200.100000 Name: ash, dtype: float64 Unique values: 155
If we look at the distribution of ash in samples with non-zero levels of ash, we see a moderate left-hand skew.
Water (water)¶
In [14]:
print_summary(raw_data['water'])
count 1030.000000 mean 181.567282 std 21.354219 min 121.800000 25% 164.900000 50% 185.000000 75% 192.000000 max 247.000000 Name: water, dtype: float64 Unique values: 195
Distribution of water has a strong left-hand skew but also several large outliers.
Superplasticizer (superplastic)¶
In [15]:
print_summary(raw_data['superplastic'])
count 1030.000000 mean 6.204660 std 5.973841 min 0.000000 25% 0.000000 50% 6.400000 75% 10.200000 max 32.200000 Name: superplastic, dtype: float64 Unique values: 111
Distribution of superplastic has a strong right skew with a few large outliers. It also has many 0-values so let's look at what the non-zero superplastic distribution looks like.
In [16]:
replace_zeros('superplastic', np.nan)
print_summary(data['superplastic'])
C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal keep = (tmp_a >= first_edge) C:\Users\pcopley\Anaconda3\lib\site-packages\numpy\lib\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal keep &= (tmp_a <= last_edge)
count 651.000000 mean 9.816897 std 4.580328 min 1.700000 25% 6.950000 50% 9.400000 75% 11.600000 max 32.200000 Name: superplastic, dtype: float64 Unique values: 110
Looking at only non-zero superplastic values, we see a right-hand skew with several outliers.
Coarse Aggregate (coarseagg)¶
In [17]:
print_summary(raw_data['coarseagg'])
count 1030.000000 mean 972.918932 std 77.753954 min 801.000000 25% 932.000000 50% 968.000000 75% 1029.400000 max 1145.000000 Name: coarseagg, dtype: float64 Unique values: 284
Distribution of courseagg has a slight right skew and no outliers.
Fine Aggregate (fineagg)¶
In [18]:
print_summary(raw_data['fineagg'])
count 1030.000000 mean 773.580485 std 80.175980 min 594.000000 25% 730.950000 50% 779.500000 75% 824.000000 max 992.600000 Name: fineagg, dtype: float64 Unique values: 302
Distribution of fineagg does not have a noticeable skew.
Age (age)¶
In [19]:
print_summary(raw_data['age'])
count 1030.000000 mean 45.662136 std 63.169912 min 1.000000 25% 7.000000 50% 28.000000 75% 56.000000 max 365.000000 Name: age, dtype: float64 Unique values: 14
Distribution of age has a right skew with several large outliers for particularly older samples.
Compressive Strength (strength)¶
In [20]:
print_summary(raw_data['strength'])
count 1030.000000 mean 35.817961 std 16.705742 min 2.330000 25% 23.710000 50% 34.445000 75% 46.135000 max 82.600000 Name: strength, dtype: float64 Unique values: 845
Distribution of strength has a slight right skew with a few large outliers.
Bivariate analyses¶
In [21]:
g = sns.PairGrid(raw_data);
g.map_upper(sns.scatterplot);
g.map_diag(sns.kdeplot);
g.map_lower(sns.kdeplot);
In [22]:
plt.figure(figsize=(15,10))
sns.heatmap(raw_data.corr(), annot=True, cmap='mako', vmin=-.658, vmax=.498, center=0, fmt='.3f');
With the raw data, the strongest positive correlations seem to be between (1) strength and cement content, (2) ash content and superplastic content, and (3) superplastic content and strength.
In [23]:
marginal_boxplot('strength', 'cement')
In [24]:
marginal_boxplot('ash', 'superplastic')
In [25]:
marginal_boxplot('strength', 'superplastic')
The strongest negative correlations seem to be between (1) superplastic content and water content, (2) fine aggregate content and water content, and (3) cement content and ash content.
In [26]:
marginal_boxplot('superplastic', 'water')
In [27]:
marginal_boxplot('fineagg', 'water')
In [28]:
marginal_boxplot('cement', 'ash')
In [29]:
plt.figure(figsize=(15,10))
sns.heatmap(data.corr(), annot=True, cmap='mako', vmin=-.538, vmax=.498, center=0, fmt='.3f');
In [30]:
# Re-run the marginal boxplots for the non-zero values just to see if there is any significant difference
marginal_boxplot('ash', 'superplastic')
marginal_boxplot('ash', 'superplastic', raw=False)
In [31]:
marginal_boxplot('strength', 'superplastic')
marginal_boxplot('strength', 'superplastic', raw=False)
In [32]:
marginal_boxplot('superplastic', 'water')
marginal_boxplot('superplastic', 'water', raw=False)
In [33]:
marginal_boxplot('cement', 'ash')
marginal_boxplot('cement', 'ash', raw=False)
There does not seem to be any significant change in correlation when we remove zero values from the raw data.
The strongest positive correlations are centered around cement content, superplastics content, and strength. Another is ash, which is positively correlated with superplastics but negatively correlated with strength. So what does the relationship between cement, superplastics, and ash look like?
For this we'll utilize a 3-dimensional scatterplot; one with the zero values, one without.
In [34]:
fig = plt.figure(figsize=(25,15))
ax = fig.add_subplot(111, projection='3d')
x = raw_data['cement']
y = raw_data['superplastic']
z = raw_data['ash']
ax.scatter(x, y, z, c='r', marker='o')
ax.set_xlabel('Cement Content')
ax.set_ylabel('Superplastics Content')
ax.set_zlabel('Ash Content')
plt.show();
In [35]:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(25,15))
ax = fig.add_subplot(111, projection='3d')
x = data['cement']
y = data['superplastic']
z = data['ash']
ax.scatter(x, y, z, c='r', marker='o')
ax.set_xlabel('Cement Content')
ax.set_ylabel('Superplastics Content')
ax.set_zlabel('Ash Content')
plt.show();
Feature Engineering Techniques¶
Feature Extraction¶
In [36]:
raw_data.head()
Out[36]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | 0.0 | 203.5 | 0.0 | 971.8 | 748.5 | 28 | 29.89 |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 |
| 2 | 250.0 | 0.0 | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 |
| 3 | 266.0 | 114.0 | 0.0 | 228.0 | 0.0 | 932.0 | 670.0 | 28 | 45.85 |
| 4 | 154.8 | 183.4 | 0.0 | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 |
One simple feature we can add is total aggregate content, which is just the sum of coarseagg and fineagg.
In [37]:
raw_data['agg'] = raw_data['coarseagg'] + raw_data['fineagg']
data['agg'] = data['coarseagg'] + data['fineagg']
In [38]:
raw_data.head()
Out[38]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | agg | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | 0.0 | 203.5 | 0.0 | 971.8 | 748.5 | 28 | 29.89 | 1720.3 |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 | 1877.0 |
| 2 | 250.0 | 0.0 | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 | 1818.1 |
| 3 | 266.0 | 114.0 | 0.0 | 228.0 | 0.0 | 932.0 | 670.0 | 28 | 45.85 | 1602.0 |
| 4 | 154.8 | 183.4 | 0.0 | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 | 1744.1 |
Additionally, since superplasticizers are synthetic compounds designed to reduce the required amount of water, perhaps we can get something useful out of the water:superplastic ratio?
In [39]:
raw_data['swr'] = raw_data['water'] / raw_data['superplastic']
data['swr'] = data['water'] / data['superplastic']
# This is more readable than superplastic:water, but we get inf when dividing by zero, so let's replace that with nan
raw_data['swr'].replace(np.inf, np.nan, inplace=True)
data['swr'].replace(np.inf, np.nan, inplace=True)
In [40]:
raw_data.head()
Out[40]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | agg | swr | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | 0.0 | 203.5 | 0.0 | 971.8 | 748.5 | 28 | 29.89 | 1720.3 | NaN |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 | 1877.0 | 14.657407 |
| 2 | 250.0 | 0.0 | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 | 1818.1 | 34.072727 |
| 3 | 266.0 | 114.0 | 0.0 | 228.0 | 0.0 | 932.0 | 670.0 | 28 | 45.85 | 1602.0 | NaN |
| 4 | 154.8 | 183.4 | 0.0 | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 | 1744.1 | 21.241758 |
Finally, maybe our model won't care how much of something was added, just that it was? So let's create a one-hot-esque column that is just a flag for whether or not that ingredient has been added.
In [41]:
# We don't need to do this for all columns, only those that have zero values
raw_data['contains_slag'] = raw_data['slag'] > 0
raw_data['contains_ash'] = raw_data['ash'] > 0
raw_data['contains_superplastic'] = raw_data['superplastic'] > 0
data['contains_slag'] = data['slag'] > 0
data['contains_ash'] = data['ash'] > 0
data['contains_superplastic'] = data['superplastic'] > 0
# Recast True/False as 1/0
raw_data['contains_slag'] = raw_data['contains_slag'].astype(int)
raw_data['contains_ash'] = raw_data['contains_ash'].astype(int)
raw_data['contains_superplastic'] = raw_data['contains_superplastic'].astype(int)
data['contains_slag'] = data['contains_slag'].astype(int)
data['contains_ash'] = data['contains_ash'].astype(int)
data['contains_superplastic'] = data['contains_superplastic'].astype(int)
In [42]:
raw_data.head()
Out[42]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | agg | swr | contains_slag | contains_ash | contains_superplastic | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | 0.0 | 203.5 | 0.0 | 971.8 | 748.5 | 28 | 29.89 | 1720.3 | NaN | 1 | 0 | 0 |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 | 1877.0 | 14.657407 | 1 | 1 | 1 |
| 2 | 250.0 | 0.0 | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 | 1818.1 | 34.072727 | 0 | 1 | 1 |
| 3 | 266.0 | 114.0 | 0.0 | 228.0 | 0.0 | 932.0 | 670.0 | 28 | 45.85 | 1602.0 | NaN | 1 | 0 | 0 |
| 4 | 154.8 | 183.4 | 0.0 | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 | 1744.1 | 21.241758 | 1 | 0 | 1 |
In [43]:
data.head()
Out[43]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | strength | agg | swr | contains_slag | contains_ash | contains_superplastic | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 141.3 | 212.0 | NaN | 203.5 | NaN | 971.8 | 748.5 | 28 | 29.89 | 1720.3 | NaN | 1 | 0 | 0 |
| 1 | 168.9 | 42.2 | 124.3 | 158.3 | 10.8 | 1080.8 | 796.2 | 14 | 23.51 | 1877.0 | 14.657407 | 1 | 1 | 1 |
| 2 | 250.0 | NaN | 95.7 | 187.4 | 5.5 | 956.9 | 861.2 | 28 | 29.22 | 1818.1 | 34.072727 | 0 | 1 | 1 |
| 3 | 266.0 | 114.0 | NaN | 228.0 | NaN | 932.0 | 670.0 | 28 | 45.85 | 1602.0 | NaN | 1 | 0 | 0 |
| 4 | 154.8 | 183.4 | NaN | 193.3 | 9.1 | 1047.4 | 696.7 | 28 | 18.29 | 1744.1 | 21.241758 | 1 | 0 | 1 |
Train/Test Split¶
In [44]:
# LinearRegression doesn't like np.nan, so we'll use raw_data
# and replace swr nan's with 0
raw_data['swr'].replace(np.nan, 0, inplace=True)
x = raw_data.drop('strength', axis=1)
y = raw_data[['strength']]
In [45]:
train_ratio = .7
x_train, x_test, y_train, y_test = tts(x, y, test_size=1-train_ratio, random_state=1)
In [46]:
labels = ['Training Score', 'Testing Score', '10-Fold CV Mean', '10-Fold CV Std. Dev.']
In [47]:
simple_linear_regression = LinearRegression()
simple_linear_regression.fit(x_train, y_train)
Out[47]:
LinearRegression()
In [48]:
for idx, col_name in enumerate(x_train.columns):
print("The coefficient for {} is {}.".format(col_name, simple_linear_regression.coef_[0][idx]))
The coefficient for cement is 0.11411851032308984. The coefficient for slag is 0.0886246888240249. The coefficient for ash is 0.015494339318093343. The coefficient for water is -0.1260184282008129. The coefficient for superplastic is -0.5300383276953631. The coefficient for coarseagg is 818789528842.1881. The coefficient for fineagg is 818789528842.1982. The coefficient for age is 0.1141598653445305. The coefficient for agg is -818789528842.1746. The coefficient for swr is -0.11369922898004697. The coefficient for contains_slag is -0.05722979299054391. The coefficient for contains_ash is 1.7396467070143116. The coefficient for contains_superplastic is 18.73162969685704.
In [49]:
intercept = simple_linear_regression.intercept_[0]
print("The intercept for our model is {}".format(intercept))
The intercept for our model is -25.35044444444444
In [50]:
slr_train = simple_linear_regression.score(x_train, y_train)
slr_test = simple_linear_regression.score(x_test, y_test)
In [51]:
slr_cv = cross_validate(simple_linear_regression, x, y.values.ravel(), cv=10)
linear_reg_series = pd.Series(data=[slr_train, slr_test, slr_cv['test_score'].mean(), slr_cv['test_score'].std()],
index=labels)
model_comparison = pd.DataFrame(linear_reg_series, columns=['Simple Linear Regression'])
model_comparison.T
Out[51]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
Clearly with a dataset this complicated, linear regression isn't going to cut it. Let's see if a decision tree performs any better.
In [52]:
dt = DecisionTreeRegressor(random_state=1)
dt.fit(x_train, y_train)
Out[52]:
DecisionTreeRegressor(random_state=1)
In [53]:
dt_train = dt.score(x_train, y_train)
dt_test = dt.score(x_test, y_test)
dt_cv = cross_validate(dt, x, y.values.ravel(), cv=10)
In [54]:
model_comparison['Decision Tree'] = pd.Series(data=[dt_train, dt_test, dt_cv['test_score'].mean(), dt_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[54]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
In [55]:
pd.DataFrame(dt.feature_importances_, columns=['Imp'], index=x_train.columns)
Out[55]:
| Imp | |
|---|---|
| cement | 0.344124 |
| slag | 0.089350 |
| ash | 0.019093 |
| water | 0.118810 |
| superplastic | 0.011971 |
| coarseagg | 0.025242 |
| fineagg | 0.006602 |
| age | 0.337260 |
| agg | 0.036446 |
| swr | 0.009485 |
| contains_slag | 0.000034 |
| contains_ash | 0.001421 |
| contains_superplastic | 0.000162 |
Much better performance than linear, but how much better will RF perform?
In [56]:
# max_features=8 gives us the best test score of 90.92
rf = RandomForestRegressor(n_estimators=50, random_state=1, max_features=8)
rf = rf.fit(x_train, y_train.values.ravel())
In [57]:
rf_train = rf.score(x_train, y_train)
rf_test = rf.score(x_test, y_test)
rf_cv = cross_validate(rf, x, y.values.ravel(), cv=10)
In [58]:
model_comparison['Random Forest'] = pd.Series(data=[rf_train, rf_test, rf_cv['test_score'].mean(), rf_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[58]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
MinMaxScaler
In [59]:
df_columns = raw_data.columns.drop('strength')
scaler = MinMaxScaler()
df = scaler.fit_transform(raw_data.drop('strength', axis=1))
df = pd.DataFrame(df)
df.columns = df_columns
df['strength'] = raw_data['strength']
df.head()
Out[59]:
| cement | slag | ash | water | superplastic | coarseagg | fineagg | age | agg | swr | contains_slag | contains_ash | contains_superplastic | strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.089726 | 0.589872 | 0.000000 | 0.652556 | 0.000000 | 0.496512 | 0.387607 | 0.074176 | 0.513255 | 0.000000 | 1.0 | 0.0 | 0.0 | 29.89 |
| 1 | 0.152740 | 0.117418 | 0.621189 | 0.291534 | 0.335404 | 0.813372 | 0.507275 | 0.035714 | 0.818713 | 0.120959 | 1.0 | 1.0 | 1.0 | 23.51 |
| 2 | 0.337900 | 0.000000 | 0.478261 | 0.523962 | 0.170807 | 0.453198 | 0.670346 | 0.074176 | 0.703899 | 0.281183 | 0.0 | 1.0 | 1.0 | 29.22 |
| 3 | 0.374429 | 0.317195 | 0.000000 | 0.848243 | 0.000000 | 0.380814 | 0.190667 | 0.074176 | 0.282651 | 0.000000 | 1.0 | 0.0 | 0.0 | 45.85 |
| 4 | 0.120548 | 0.510295 | 0.000000 | 0.571086 | 0.282609 | 0.716279 | 0.257652 | 0.074176 | 0.559649 | 0.175296 | 1.0 | 0.0 | 1.0 | 18.29 |
In [60]:
x = df.drop('strength', axis=1)
y = df[['strength']]
x_train, x_test, y_train, y_test = tts(x, y, test_size=1-train_ratio, random_state=1)
# max_features=6 gives us the best test score of 90.67
rf2 = RandomForestRegressor(n_estimators=50, max_features=6, random_state=1)
rf2 = rf2.fit(x_train, y_train.values.ravel())
rf_mms_train = rf2.score(x_train, y_train)
rf_mms_test = rf2.score(x_test, y_test)
rf_mms_cv = cross_validate(rf2, x, y.values.ravel(), cv=10)
In [61]:
model_comparison['Random Forest with MinMaxScaler'] = pd.Series(data=[rf_mms_train, rf_mms_test, rf_mms_cv['test_score'].mean(), rf_mms_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[61]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
Utilizing MinMaxScaler() gives us nearly identical performance, but only using 6 features instead of 8.
GradientBoostingRegressor
In [62]:
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
gbr = GradientBoostingRegressor()
gbr = gbr.fit(x_train, y_train.values.ravel())
In [63]:
gbr_train = gbr.score(x_train, y_train)
gbr_test = gbr.score(x_test, y_test)
gbr_cv = cross_validate(gbr, x, y.values.ravel(), cv=10)
In [64]:
model_comparison['Gradient Boosting Regressor'] = pd.Series(data=[gbr_train, gbr_test, gbr_cv['test_score'].mean(), gbr_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[64]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
RandomizedSearchCV
In [65]:
print('RandomForest parameters (no MinMaxScaler):\n')
print(rf.get_params())
RandomForest parameters (no MinMaxScaler):
{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'mse', 'max_depth': None, 'max_features': 8, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 1, 'verbose': 0, 'warm_start': False}
In [66]:
random_grid = {
'n_estimators': [int(x) for x in np.linspace(start=50, stop=2000, num=25)],
'max_features': ['auto', 'sqrt'],
'max_depth': [int(x) for x in np.linspace(10, 110, num=11)],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4],
'bootstrap': [True, False]
}
print(random_grid)
{'n_estimators': [50, 131, 212, 293, 375, 456, 537, 618, 700, 781, 862, 943, 1025, 1106, 1187, 1268, 1350, 1431, 1512, 1593, 1675, 1756, 1837, 1918, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]}
In [67]:
rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid, n_iter=100, cv=3, verbose=2, random_state=1, n_jobs=-1)
In [68]:
rf_random.fit(x_train, y_train.values.ravel())
Fitting 3 folds for each of 100 candidates, totalling 300 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 45.2s [Parallel(n_jobs=-1)]: Done 154 tasks | elapsed: 3.1min [Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed: 6.1min finished
Out[68]:
RandomizedSearchCV(cv=3,
estimator=RandomForestRegressor(max_features=8,
n_estimators=50,
random_state=1),
n_iter=100, n_jobs=-1,
param_distributions={'bootstrap': [True, False],
'max_depth': [10, 20, 30, 40, 50, 60,
70, 80, 90, 100, 110],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'n_estimators': [50, 131, 212, 293, 375,
456, 537, 618, 700,
781, 862, 943, 1025,
1106, 1187, 1268, 1350,
1431, 1512, 1593, 1675,
1756, 1837, 1918,
2000]},
random_state=1, verbose=2)
In [69]:
rf_random.best_params_
Out[69]:
{'n_estimators': 1756,
'min_samples_split': 2,
'min_samples_leaf': 1,
'max_features': 'auto',
'max_depth': 110,
'bootstrap': True}
In [70]:
rf_best_random = rf_random.best_estimator_
In [71]:
def evaluate(model, test_features, test_labels):
predictions = model.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * np.mean(errors / test_labels)
accuracy = 100 - mape
print('Model Performance')
print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))
print('Accuracy: {:0.2f}%.'.format(accuracy))
return accuracy
In [72]:
base_model = RandomForestRegressor(n_estimators=50, random_state=1, max_features=8)
base_model.fit(x_train, y_train.values.ravel())
base_model.score(x_test, y_test.values.ravel())
Out[72]:
RandomForestRegressor(max_features=8, n_estimators=50, random_state=1)
Out[72]:
0.9091278108955855
In [73]:
rf_rscv_train = rf_best_random.score(x_train, y_train.values.ravel())
rf_rscv_test = rf_best_random.score(x_test, y_test.values.ravel())
rfrs_cv = cross_validate(rf_best_random, x, y.values.ravel(), cv=10)
In [74]:
model_comparison['Randomized Search CV'] = pd.Series(data=[rf_rscv_train, rf_rscv_test, rfrs_cv['test_score'].mean(), rfrs_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[74]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
| Randomized Search CV | 0.983311 | 0.905970 | 0.917427 | 0.024922 |
GridSearchCV
In [75]:
param_grid = {
'bootstrap': [True],
'max_depth': [100, 110, 120],
'max_features': ['auto'],
'min_samples_leaf': [1, 2],
'min_samples_split': [1, 2, 3],
'n_estimators': [1650, 1750, 1850]
}
grid_model_base = RandomForestRegressor(random_state=1)
grid_search = GridSearchCV(estimator=grid_model_base, param_grid=param_grid, cv=3, n_jobs=-1, verbose=2)
In [76]:
grid_search.fit(x_train, y_train.values.ravel())
Fitting 3 folds for each of 54 candidates, totalling 162 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 49.4s [Parallel(n_jobs=-1)]: Done 154 tasks | elapsed: 4.3min [Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed: 4.6min finished
Out[76]:
GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=1), n_jobs=-1,
param_grid={'bootstrap': [True], 'max_depth': [100, 110, 120],
'max_features': ['auto'], 'min_samples_leaf': [1, 2],
'min_samples_split': [1, 2, 3],
'n_estimators': [1650, 1750, 1850]},
verbose=2)
In [77]:
grid_search.best_params_
Out[77]:
{'bootstrap': True,
'max_depth': 100,
'max_features': 'auto',
'min_samples_leaf': 1,
'min_samples_split': 2,
'n_estimators': 1850}
In [78]:
rf_gscv_train = grid_search.best_estimator_.score(x_train, y_train.values.ravel())
rf_gscv_test = grid_search.best_estimator_.score(x_test, y_test.values.ravel())
rfgs_cv = cross_validate(grid_search.best_estimator_, x, y.values.ravel(), cv=10)
In [79]:
model_comparison['Grid Search CV'] = pd.Series(data=[rf_gscv_train, rf_gscv_test, rfgs_cv['test_score'].mean(), rfgs_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[79]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
| Randomized Search CV | 0.983311 | 0.905970 | 0.917427 | 0.024922 |
| Grid Search CV | 0.983307 | 0.905936 | 0.917525 | 0.024961 |
XGBoost
In [80]:
from xgboost import XGBRegressor
In [81]:
xgb = XGBRegressor()
xgb.fit(x_train, y_train.values.ravel())
Out[81]:
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.300000012, max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
tree_method='exact', validate_parameters=1, verbosity=None)
In [82]:
xgb_train = xgb.score(x_train, y_train.values.ravel())
xgb_test = xgb.score(x_test, y_test.values.ravel())
xgb_cv = cross_validate(xgb, x, y.values.ravel(), cv=10)
In [83]:
model_comparison['XGBoost'] = pd.Series(data=[xgb_train, xgb_test, xgb_cv['test_score'].mean(), xgb_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[83]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
| Randomized Search CV | 0.983311 | 0.905970 | 0.917427 | 0.024922 |
| Grid Search CV | 0.983307 | 0.905936 | 0.917525 | 0.024961 |
| XGBoost | 0.994335 | 0.916113 | 0.933395 | 0.027554 |
In [84]:
from xgboost import plot_importance
plot_importance(xgb)
plt.show()
Out[84]:
<matplotlib.axes._subplots.AxesSubplot at 0x19f745e8280>
In [85]:
from xgboost import plot_tree
plot_tree(xgb, num_trees=1)
fig = plt.gcf()
fig.set_size_inches(75,75);
In [86]:
xgb.get_params()
Out[86]:
{'objective': 'reg:squarederror',
'base_score': 0.5,
'booster': 'gbtree',
'colsample_bylevel': 1,
'colsample_bynode': 1,
'colsample_bytree': 1,
'gamma': 0,
'gpu_id': -1,
'importance_type': 'gain',
'interaction_constraints': '',
'learning_rate': 0.300000012,
'max_delta_step': 0,
'max_depth': 6,
'min_child_weight': 1,
'missing': nan,
'monotone_constraints': '()',
'n_estimators': 100,
'n_jobs': 0,
'num_parallel_tree': 1,
'random_state': 0,
'reg_alpha': 0,
'reg_lambda': 1,
'scale_pos_weight': 1,
'subsample': 1,
'tree_method': 'exact',
'validate_parameters': 1,
'verbosity': None}
In [87]:
from scipy import stats
otl = stats.beta(10,1)
zp = stats.expon(0,50)
params = {
"n_estimators": stats.randint(3,40),
"max_depth": stats.randint(3,40),
"learning_rate": stats.uniform(.05, .4),
"colsample_bytree": otl,
"subsample": otl,
"gamma": stats.uniform(0,10),
"reg_alpha": zp,
"min_child_weight": zp
}
xgbreg = XGBRegressor()
In [88]:
xgbrs = RandomizedSearchCV(n_iter=100, cv=3, verbose=2, random_state=1, estimator=xgbreg, param_distributions=params, n_jobs=1)
xgbrs.fit(x_train, y_train)
Fitting 3 folds for each of 100 candidates, totalling 300 fits [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679 [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679, total= 0.0s [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679 [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679, total= 0.0s [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679 [CV] colsample_bytree=0.9775097845509342, gamma=1.4675589081711304, learning_rate=0.08693543790751912, max_depth=15, min_child_weight=24.543857966503296, n_estimators=9, reg_alpha=137.08479938255715, subsample=0.9545149598867679, total= 0.0s [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537 [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537, total= 0.0s [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537 [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537, total= 0.0s [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537 [CV] colsample_bytree=0.8223966978097618, gamma=9.13962024579233, learning_rate=0.23288192319479534, max_depth=33, min_child_weight=11.038661205836513, n_estimators=16, reg_alpha=62.93386145983712, subsample=0.9929462979076537, total= 0.0s [CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986 [CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986, total= 0.0s [CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986 [CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986, total= 0.0s
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986 [CV] colsample_bytree=0.8846904207054818, gamma=2.7304997421674737, learning_rate=0.0736972805206254, max_depth=11, min_child_weight=5.1762674654770375, n_estimators=6, reg_alpha=55.68438226239254, subsample=0.9781240906789986, total= 0.0s [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251 [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251, total= 0.0s [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251 [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251, total= 0.0s [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251 [CV] colsample_bytree=0.934051018182523, gamma=0.3417131118583905, learning_rate=0.29961199459094134, max_depth=3, min_child_weight=68.94918831379002, n_estimators=37, reg_alpha=77.86109270343921, subsample=0.9337356367900251, total= 0.0s [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872 [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872, total= 0.0s [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872 [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872, total= 0.0s [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872 [CV] colsample_bytree=0.9845172071896382, gamma=0.19366957870297075, learning_rate=0.3215342131759564, max_depth=11, min_child_weight=120.64700401049453, n_estimators=13, reg_alpha=33.8216973284279, subsample=0.9304027434041872, total= 0.0s [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047 [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047, total= 0.0s [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047 [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047, total= 0.0s [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047 [CV] colsample_bytree=0.989870219416199, gamma=4.140559878195683, learning_rate=0.32776006309109806, max_depth=6, min_child_weight=38.89175481553912, n_estimators=16, reg_alpha=2.3203011748083013, subsample=0.808945392894047, total= 0.0s [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462 [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462, total= 0.0s [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462 [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462, total= 0.0s [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462 [CV] colsample_bytree=0.8994859359482075, gamma=5.384246942799851, learning_rate=0.31091955282541683, max_depth=21, min_child_weight=50.78297338250841, n_estimators=13, reg_alpha=107.41004682371951, subsample=0.9210458822708462, total= 0.0s [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966 [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966, total= 0.0s [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966 [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966, total= 0.0s [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966 [CV] colsample_bytree=0.9170979308856062, gamma=9.648400471483855, learning_rate=0.31537659912737925, max_depth=36, min_child_weight=3.8328229919214216, n_estimators=31, reg_alpha=149.2784633224072, subsample=0.8937097060052966, total= 0.0s [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066 [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066, total= 0.0s [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066 [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066, total= 0.0s [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066 [CV] colsample_bytree=0.8725343518115356, gamma=0.0287032703115897, learning_rate=0.2968579654482896, max_depth=23, min_child_weight=43.21266499614963, n_estimators=25, reg_alpha=108.55245293714539, subsample=0.7737890364779066, total= 0.0s [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472 [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472, total= 0.0s [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472 [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472, total= 0.0s [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472 [CV] colsample_bytree=0.8900819622421665, gamma=9.973228504514806, learning_rate=0.11893620333813143, max_depth=28, min_child_weight=67.88822772352026, n_estimators=16, reg_alpha=59.67112631904906, subsample=0.873299979645472, total= 0.0s [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436 [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436, total= 0.0s [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436 [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436, total= 0.0s [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436 [CV] colsample_bytree=0.9872761317037585, gamma=0.19880133839795588, learning_rate=0.06048439475108772, max_depth=24, min_child_weight=35.648138769731304, n_estimators=30, reg_alpha=98.3156254875721, subsample=0.92579916634436, total= 0.0s [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857 [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857, total= 0.0s [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857 [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857, total= 0.0s [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857 [CV] colsample_bytree=0.9344965699335187, gamma=0.1864728937294302, learning_rate=0.37025306907224653, max_depth=32, min_child_weight=82.28051468466589, n_estimators=23, reg_alpha=27.835610118077707, subsample=0.9074252279611857, total= 0.0s [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887 [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887, total= 0.0s [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887 [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887, total= 0.0s [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887 [CV] colsample_bytree=0.636426477222251, gamma=9.164098732731798, learning_rate=0.3627833849957864, max_depth=16, min_child_weight=62.41173340402217, n_estimators=3, reg_alpha=0.6317736291227005, subsample=0.6683557352982887, total= 0.0s [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578 [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578, total= 0.1s [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578 [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578, total= 0.1s [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578 [CV] colsample_bytree=0.814884396584042, gamma=1.954294811093188, learning_rate=0.2825435709093031, max_depth=38, min_child_weight=2.565616617293441, n_estimators=37, reg_alpha=11.430305187578542, subsample=0.8269376740488578, total= 0.1s [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438 [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438, total= 0.0s [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438 [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438, total= 0.0s [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438 [CV] colsample_bytree=0.8778200654676123, gamma=7.625731346670644, learning_rate=0.23211298679519415, max_depth=13, min_child_weight=5.7894051446014565, n_estimators=37, reg_alpha=23.91317623249975, subsample=0.9111970919398438, total= 0.0s [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153 [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153, total= 0.0s [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153 [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153, total= 0.0s [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153 [CV] colsample_bytree=0.9326188578145767, gamma=6.297175070215645, learning_rate=0.13406960396593584, max_depth=28, min_child_weight=8.84912238209576, n_estimators=3, reg_alpha=51.007740731755845, subsample=0.829697064288153, total= 0.0s [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874 [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874, total= 0.0s [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874 [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874, total= 0.0s [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874 [CV] colsample_bytree=0.8601544015579499, gamma=9.07815852503524, learning_rate=0.42278882767873494, max_depth=6, min_child_weight=27.826974731547615, n_estimators=19, reg_alpha=47.95708774197891, subsample=0.7572043377602874, total= 0.0s [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146 [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146, total= 0.0s [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146 [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146, total= 0.0s [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146 [CV] colsample_bytree=0.9619178716928831, gamma=4.859906670969098, learning_rate=0.2917241931679893, max_depth=25, min_child_weight=130.30724541975977, n_estimators=15, reg_alpha=22.973117534909058, subsample=0.9964736672519146, total= 0.0s [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275 [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275, total= 0.0s [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275 [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275, total= 0.0s [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275 [CV] colsample_bytree=0.9983645395087906, gamma=4.595452841220066, learning_rate=0.43452690490220625, max_depth=36, min_child_weight=0.7566453892334835, n_estimators=8, reg_alpha=20.178114059119896, subsample=0.8443765778231275, total= 0.0s [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163 [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163, total= 0.0s [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163 [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163, total= 0.0s [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163 [CV] colsample_bytree=0.8424955340723784, gamma=8.447344453922218, learning_rate=0.4121569274834597, max_depth=6, min_child_weight=28.103130129138815, n_estimators=22, reg_alpha=16.82393145347256, subsample=0.909026787249163, total= 0.0s [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614 [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614, total= 0.0s [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614 [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614, total= 0.0s [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614 [CV] colsample_bytree=0.8561630272635711, gamma=3.1524480309537295, learning_rate=0.4071554834100605, max_depth=27, min_child_weight=39.25185668087049, n_estimators=32, reg_alpha=77.54176286499845, subsample=0.9938666866647614, total= 0.0s [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628 [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628, total= 0.0s [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628 [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628, total= 0.0s [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628 [CV] colsample_bytree=0.9999752854853113, gamma=9.767591490310597, learning_rate=0.2006321258983098, max_depth=24, min_child_weight=4.2917801169367396, n_estimators=19, reg_alpha=42.749376394757945, subsample=0.9566354330390628, total= 0.0s [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788 [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788, total= 0.0s [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788 [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788, total= 0.0s [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788 [CV] colsample_bytree=0.9150263918333182, gamma=6.98057248447303, learning_rate=0.39579177202183996, max_depth=28, min_child_weight=15.340071575524005, n_estimators=5, reg_alpha=24.071655038562053, subsample=0.9568662605812788, total= 0.0s [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417 [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417, total= 0.0s [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417 [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417, total= 0.0s [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417 [CV] colsample_bytree=0.7830976270594145, gamma=6.778008914343111, learning_rate=0.12942795537084434, max_depth=14, min_child_weight=0.5025028937115578, n_estimators=35, reg_alpha=79.88505395560864, subsample=0.7085561122327417, total= 0.0s [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704 [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704, total= 0.0s [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704 [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704, total= 0.0s [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704 [CV] colsample_bytree=0.7657053206780995, gamma=5.277146463087466, learning_rate=0.37086443360183924, max_depth=21, min_child_weight=4.3621890684654065, n_estimators=33, reg_alpha=42.06588776117605, subsample=0.9679036217617704, total= 0.0s [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698 [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698, total= 0.0s [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698 [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698, total= 0.0s [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698 [CV] colsample_bytree=0.6917736908605385, gamma=1.8161285133076377, learning_rate=0.3747434790882159, max_depth=33, min_child_weight=3.79673720087531, n_estimators=31, reg_alpha=42.13974883372369, subsample=0.9256299657300698, total= 0.0s [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887 [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887, total= 0.0s [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887 [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887, total= 0.0s [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887 [CV] colsample_bytree=0.9450387383654071, gamma=9.163055534683508, learning_rate=0.41385420998062283, max_depth=17, min_child_weight=5.876788978186174, n_estimators=20, reg_alpha=34.61579337770725, subsample=0.970726466955887, total= 0.0s [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685 [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685, total= 0.0s [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685 [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685, total= 0.0s [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685 [CV] colsample_bytree=0.924371319778363, gamma=2.3366613923925006, learning_rate=0.09078690377031898, max_depth=5, min_child_weight=78.12140055629224, n_estimators=35, reg_alpha=8.283349507145983, subsample=0.9140685768819685, total= 0.0s [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978 [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978, total= 0.0s [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978 [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978, total= 0.0s [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978 [CV] colsample_bytree=0.9656665471056349, gamma=5.19351824366033, learning_rate=0.3641184112886476, max_depth=38, min_child_weight=18.576946077130895, n_estimators=18, reg_alpha=4.211692680168841, subsample=0.9999075984465978, total= 0.0s [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992 [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992, total= 0.0s [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992 [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992, total= 0.0s [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992 [CV] colsample_bytree=0.9425522100412647, gamma=0.47318232441725727, learning_rate=0.3393459013209253, max_depth=20, min_child_weight=47.11130994476061, n_estimators=30, reg_alpha=44.017135477067775, subsample=0.968999053589992, total= 0.0s [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314 [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314, total= 0.0s [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314 [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314, total= 0.0s [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314 [CV] colsample_bytree=0.8167406971208426, gamma=5.126616988043951, learning_rate=0.29610597426518626, max_depth=6, min_child_weight=232.6748868841225, n_estimators=36, reg_alpha=7.716208058769659, subsample=0.9067978731045314, total= 0.0s [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511 [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511, total= 0.0s [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511 [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511, total= 0.0s [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511 [CV] colsample_bytree=0.9258153285652542, gamma=5.264259339055213, learning_rate=0.10417116122886796, max_depth=32, min_child_weight=1.3284201131548217, n_estimators=32, reg_alpha=43.05219742707525, subsample=0.964807626324511, total= 0.0s [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617 [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617, total= 0.0s [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617 [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617, total= 0.0s [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617 [CV] colsample_bytree=0.9493404422009409, gamma=8.414100590490124, learning_rate=0.2722110139774603, max_depth=35, min_child_weight=11.401914629021704, n_estimators=35, reg_alpha=52.02951670166972, subsample=0.9812982557044617, total= 0.0s [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294 [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294, total= 0.0s [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294 [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294, total= 0.0s [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294 [CV] colsample_bytree=0.962959330606662, gamma=4.890072943836898, learning_rate=0.13146973131129874, max_depth=34, min_child_weight=92.84384515314294, n_estimators=22, reg_alpha=34.957913776821954, subsample=0.8500481920610294, total= 0.0s [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703 [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703, total= 0.0s [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703 [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703, total= 0.0s [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703 [CV] colsample_bytree=0.9356221125253228, gamma=1.3242963302881627, learning_rate=0.19883507176503223, max_depth=25, min_child_weight=35.23328526088029, n_estimators=4, reg_alpha=4.2492514733560665, subsample=0.984374411206703, total= 0.0s [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331 [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331, total= 0.0s [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331 [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331, total= 0.0s [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331 [CV] colsample_bytree=0.9046973777610755, gamma=3.424541198435198, learning_rate=0.38834059321107106, max_depth=27, min_child_weight=85.12384192430562, n_estimators=36, reg_alpha=0.5672209744063974, subsample=0.9247620384982331, total= 0.0s [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971 [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971, total= 0.0s [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971 [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971, total= 0.0s [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971 [CV] colsample_bytree=0.9651894037352509, gamma=9.586999025558045, learning_rate=0.4447771262969091, max_depth=21, min_child_weight=30.11094999860071, n_estimators=14, reg_alpha=44.01638412606923, subsample=0.9944472853908971, total= 0.0s [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272 [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272, total= 0.0s [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272 [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272, total= 0.0s [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272 [CV] colsample_bytree=0.9749001820628698, gamma=7.762146287068875, learning_rate=0.4060665213941543, max_depth=31, min_child_weight=123.41682672745611, n_estimators=36, reg_alpha=47.76512363859637, subsample=0.9284848200851272, total= 0.0s [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387 [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387, total= 0.0s [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387 [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387, total= 0.0s [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387 [CV] colsample_bytree=0.9778015162224186, gamma=9.997691169446238, learning_rate=0.2794927604492029, max_depth=38, min_child_weight=33.637785648614084, n_estimators=5, reg_alpha=83.7283205207771, subsample=0.97079092568387, total= 0.0s [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985 [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985, total= 0.0s [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985 [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985, total= 0.0s [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985 [CV] colsample_bytree=0.962838002424029, gamma=5.336533449712114, learning_rate=0.17578680447433986, max_depth=27, min_child_weight=6.635445874743752, n_estimators=30, reg_alpha=28.42205951066925, subsample=0.8218092655515985, total= 0.0s [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513 [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513, total= 0.0s [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513 [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513, total= 0.0s [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513 [CV] colsample_bytree=0.8498504401772669, gamma=6.902278681627479, learning_rate=0.231141399980615, max_depth=6, min_child_weight=17.131336453946968, n_estimators=32, reg_alpha=112.5454845103544, subsample=0.8596416843808513, total= 0.0s [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245 [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245, total= 0.0s [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245 [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245, total= 0.0s [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245 [CV] colsample_bytree=0.7694156747825355, gamma=0.5944907614485251, learning_rate=0.40459979455729833, max_depth=23, min_child_weight=93.00082832642657, n_estimators=7, reg_alpha=22.034676954560886, subsample=0.8786947404965245, total= 0.0s [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621 [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621, total= 0.0s [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621 [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621, total= 0.0s [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621 [CV] colsample_bytree=0.8095793735045576, gamma=4.784503456129699, learning_rate=0.20966135876466158, max_depth=25, min_child_weight=12.991263135531565, n_estimators=34, reg_alpha=7.466469039520882, subsample=0.9699481172402621, total= 0.0s [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812 [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812, total= 0.0s [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812 [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812, total= 0.0s [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812 [CV] colsample_bytree=0.952564311302402, gamma=1.5599250030509426, learning_rate=0.1716843304084616, max_depth=8, min_child_weight=4.912465009470827, n_estimators=30, reg_alpha=105.87004739595281, subsample=0.9159861181947812, total= 0.0s [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902 [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902, total= 0.0s [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902 [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902, total= 0.0s [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902 [CV] colsample_bytree=0.9809643582069478, gamma=6.248070796484706, learning_rate=0.07251746796632506, max_depth=29, min_child_weight=21.770919515440852, n_estimators=38, reg_alpha=44.00785071169654, subsample=0.9234221873035902, total= 0.0s [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653 [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653, total= 0.0s [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653 [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653, total= 0.0s [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653 [CV] colsample_bytree=0.5983491956727909, gamma=1.6577417567546826, learning_rate=0.23516803484770066, max_depth=35, min_child_weight=1.8896609435101328, n_estimators=4, reg_alpha=6.666430265744454, subsample=0.9250726574192653, total= 0.0s [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973 [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973, total= 0.0s [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973 [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973, total= 0.0s [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973 [CV] colsample_bytree=0.9982956511648968, gamma=5.190823222119895, learning_rate=0.37399548426496654, max_depth=21, min_child_weight=184.42659907017907, n_estimators=27, reg_alpha=58.04697908379749, subsample=0.8306848440514973, total= 0.0s [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505 [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505, total= 0.0s [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505 [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505, total= 0.0s [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505 [CV] colsample_bytree=0.928923551364681, gamma=6.736984238986317, learning_rate=0.28139983191568024, max_depth=35, min_child_weight=81.89478220134201, n_estimators=8, reg_alpha=21.726390605557174, subsample=0.9777018059335505, total= 0.0s [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416 [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416, total= 0.0s [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416 [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416, total= 0.0s [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416 [CV] colsample_bytree=0.9285499018334513, gamma=8.076984091122094, learning_rate=0.19141394506719617, max_depth=7, min_child_weight=92.76899823664898, n_estimators=26, reg_alpha=187.4972937471552, subsample=0.9866161327035416, total= 0.0s [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581 [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581, total= 0.0s [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581 [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581, total= 0.0s [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581 [CV] colsample_bytree=0.9375905300738867, gamma=7.339429857330612, learning_rate=0.12390216622030842, max_depth=31, min_child_weight=53.94974491014385, n_estimators=15, reg_alpha=9.201330238445792, subsample=0.8689496700512581, total= 0.0s [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508 [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508, total= 0.0s [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508 [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508, total= 0.0s [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508 [CV] colsample_bytree=0.9420989706632965, gamma=4.84743784631088, learning_rate=0.2971593794925696, max_depth=12, min_child_weight=61.21758661968412, n_estimators=21, reg_alpha=3.5194407158563146, subsample=0.9238702912817508, total= 0.0s [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848 [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848, total= 0.0s [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848 [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848, total= 0.0s [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848 [CV] colsample_bytree=0.9526617655226074, gamma=3.2881926543503193, learning_rate=0.18762607630713307, max_depth=4, min_child_weight=51.62657285647335, n_estimators=10, reg_alpha=116.77148513799638, subsample=0.8462795211256848, total= 0.0s [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473 [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473, total= 0.0s [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473 [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473, total= 0.0s [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473 [CV] colsample_bytree=0.9952208110058465, gamma=3.6013266403457846, learning_rate=0.08040703363883908, max_depth=5, min_child_weight=20.626252034146358, n_estimators=8, reg_alpha=90.17286185247367, subsample=0.9516457485929473, total= 0.0s [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924 [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924, total= 0.0s [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924 [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924, total= 0.0s [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924 [CV] colsample_bytree=0.9468350325622289, gamma=5.306974384761395, learning_rate=0.24764650635853003, max_depth=8, min_child_weight=11.248928334409172, n_estimators=34, reg_alpha=93.97842249401404, subsample=0.9057809658772924, total= 0.0s [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475 [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475, total= 0.0s [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475 [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475, total= 0.0s [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475 [CV] colsample_bytree=0.7855223860034094, gamma=6.865631792310277, learning_rate=0.41797889109586567, max_depth=22, min_child_weight=10.511733271788604, n_estimators=8, reg_alpha=43.86550084469945, subsample=0.97371604131475, total= 0.0s [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049 [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049, total= 0.0s [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049 [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049, total= 0.0s [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049 [CV] colsample_bytree=0.9093127523974813, gamma=2.6126421140109413, learning_rate=0.44282223290137973, max_depth=38, min_child_weight=20.448993143029096, n_estimators=8, reg_alpha=71.09544088465093, subsample=0.5126573340214049, total= 0.0s [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101 [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101, total= 0.0s [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101 [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101, total= 0.0s [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101 [CV] colsample_bytree=0.869127063656987, gamma=3.748759435368594, learning_rate=0.35812901013198306, max_depth=28, min_child_weight=60.405548175429566, n_estimators=27, reg_alpha=3.1917422946152985, subsample=0.9734154918059101, total= 0.0s [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611 [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611, total= 0.0s [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611 [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611, total= 0.0s [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611 [CV] colsample_bytree=0.959149827359648, gamma=9.099835148679354, learning_rate=0.12146655184974203, max_depth=33, min_child_weight=16.8934760909329, n_estimators=15, reg_alpha=6.514209188142021, subsample=0.844802145394611, total= 0.0s [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937 [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937, total= 0.0s [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937 [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937, total= 0.0s [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937 [CV] colsample_bytree=0.9136250407720526, gamma=5.9858045984580235, learning_rate=0.1901816865055907, max_depth=24, min_child_weight=12.518161908868006, n_estimators=17, reg_alpha=117.64662065867708, subsample=0.8673342615184937, total= 0.0s [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219 [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219, total= 0.0s [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219 [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219, total= 0.0s [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219 [CV] colsample_bytree=0.9776013124075571, gamma=9.500317136335445, learning_rate=0.10019843409353944, max_depth=37, min_child_weight=120.01650549069952, n_estimators=31, reg_alpha=72.78749281655675, subsample=0.9616072201019219, total= 0.0s [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077 [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077, total= 0.0s [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077 [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077, total= 0.0s [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077 [CV] colsample_bytree=0.9820437330363218, gamma=8.479393924434762, learning_rate=0.15065565694234112, max_depth=32, min_child_weight=41.866048375706455, n_estimators=35, reg_alpha=148.63623768478072, subsample=0.8460776026451077, total= 0.0s [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244 [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244, total= 0.0s [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244 [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244, total= 0.0s [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244 [CV] colsample_bytree=0.9990000765046462, gamma=8.227300054219356, learning_rate=0.3944846553467178, max_depth=31, min_child_weight=44.27167861486731, n_estimators=7, reg_alpha=38.00909720209803, subsample=0.8555345007117244, total= 0.0s [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052 [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052, total= 0.0s [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052 [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052, total= 0.0s [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052 [CV] colsample_bytree=0.8738815719613293, gamma=0.6806776224069766, learning_rate=0.204639451222843, max_depth=38, min_child_weight=10.508386556149583, n_estimators=21, reg_alpha=140.11635207680172, subsample=0.9001976575226052, total= 0.0s [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649 [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649, total= 0.0s [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649 [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649, total= 0.0s [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649 [CV] colsample_bytree=0.9997682499504452, gamma=0.14003556274473028, learning_rate=0.13704444107956004, max_depth=3, min_child_weight=59.497613362937585, n_estimators=23, reg_alpha=26.084460047321635, subsample=0.9745800960774649, total= 0.0s [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339 [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339, total= 0.0s [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339 [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339, total= 0.0s [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339 [CV] colsample_bytree=0.9620185833498037, gamma=4.0692199412935794, learning_rate=0.44008893697492485, max_depth=26, min_child_weight=7.910222863563258, n_estimators=26, reg_alpha=37.20856994634564, subsample=0.9640008487287339, total= 0.0s [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102 [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102, total= 0.0s [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102 [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102, total= 0.0s [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102 [CV] colsample_bytree=0.9985710074383568, gamma=6.137196744254004, learning_rate=0.06247863679340147, max_depth=21, min_child_weight=15.643165353514888, n_estimators=24, reg_alpha=44.81080275678912, subsample=0.78115836934102, total= 0.0s [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472 [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472, total= 0.0s [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472 [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472, total= 0.0s [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472 [CV] colsample_bytree=0.7692563952836217, gamma=2.4699335469458283, learning_rate=0.27182224227820995, max_depth=7, min_child_weight=127.46797423947851, n_estimators=16, reg_alpha=46.93080163156149, subsample=0.9690271672470472, total= 0.0s [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391 [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391, total= 0.0s [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391 [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391, total= 0.0s [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391 [CV] colsample_bytree=0.9578430759392847, gamma=7.797442954826, learning_rate=0.11989452513096298, max_depth=23, min_child_weight=73.84840457229788, n_estimators=38, reg_alpha=63.07494325399403, subsample=0.8849652689489391, total= 0.0s [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024 [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024, total= 0.1s [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024 [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024, total= 0.1s [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024 [CV] colsample_bytree=0.9577483201693804, gamma=3.719221216676872, learning_rate=0.1921231646480489, max_depth=36, min_child_weight=49.93562742397889, n_estimators=38, reg_alpha=24.601536714807573, subsample=0.9736157364282024, total= 0.0s [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807 [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807, total= 0.0s [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807 [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807, total= 0.0s [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807 [CV] colsample_bytree=0.9062642108186547, gamma=9.82248957923346, learning_rate=0.4270179791120658, max_depth=24, min_child_weight=59.059560964959545, n_estimators=22, reg_alpha=20.961011968115272, subsample=0.8487860764282807, total= 0.0s [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909 [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909, total= 0.0s [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909 [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909, total= 0.0s [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909 [CV] colsample_bytree=0.9822102559934811, gamma=5.096132942175586, learning_rate=0.16877883431476182, max_depth=13, min_child_weight=84.02904188235755, n_estimators=34, reg_alpha=6.662360225871086, subsample=0.7707132413386909, total= 0.0s [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516 [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516, total= 0.0s [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516 [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516, total= 0.0s [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516 [CV] colsample_bytree=0.9086700861484762, gamma=5.656621125439275, learning_rate=0.23746633506450082, max_depth=27, min_child_weight=52.619928019401854, n_estimators=29, reg_alpha=69.53943033083029, subsample=0.8121353818851516, total= 0.0s [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267 [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267, total= 0.0s [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267 [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267, total= 0.0s [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267 [CV] colsample_bytree=0.9930835787468935, gamma=2.8462388100694214, learning_rate=0.07441467244786844, max_depth=33, min_child_weight=12.744852760095585, n_estimators=3, reg_alpha=23.787452591152984, subsample=0.8689177375584267, total= 0.0s [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419 [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419, total= 0.0s [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419 [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419, total= 0.0s [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419 [CV] colsample_bytree=0.9478168458985571, gamma=0.28490849491567216, learning_rate=0.3076928079516127, max_depth=18, min_child_weight=13.016643231882238, n_estimators=34, reg_alpha=178.96307930826396, subsample=0.8963554809961419, total= 0.0s [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013 [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013, total= 0.0s [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013 [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013, total= 0.0s [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013 [CV] colsample_bytree=0.857809552604072, gamma=6.741718974333468, learning_rate=0.1413670964405497, max_depth=8, min_child_weight=35.77432109356246, n_estimators=16, reg_alpha=40.64238067539015, subsample=0.6009037853772013, total= 0.0s [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847 [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847, total= 0.0s [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847 [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847, total= 0.0s [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847 [CV] colsample_bytree=0.9200931055241055, gamma=1.8046850899511557, learning_rate=0.3023836801358122, max_depth=30, min_child_weight=103.93311275507797, n_estimators=8, reg_alpha=87.23728073393639, subsample=0.9690801740743847, total= 0.0s [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561 [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561, total= 0.1s [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561 [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561, total= 0.0s [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561 [CV] colsample_bytree=0.8079425311998424, gamma=4.607424566663337, learning_rate=0.07851984345022736, max_depth=36, min_child_weight=47.08877215425798, n_estimators=37, reg_alpha=37.49844668093124, subsample=0.9672553496087561, total= 0.0s [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474 [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474, total= 0.0s [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474 [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474, total= 0.0s [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474 [CV] colsample_bytree=0.9640945047746815, gamma=5.035095098483965, learning_rate=0.4363732260078889, max_depth=9, min_child_weight=12.74989540147968, n_estimators=31, reg_alpha=34.65059659524285, subsample=0.9799420324585474, total= 0.1s [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541 [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541, total= 0.0s [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541 [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541, total= 0.0s [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541 [CV] colsample_bytree=0.9771631728950947, gamma=7.67777192147274, learning_rate=0.1661191988247473, max_depth=4, min_child_weight=58.526147523531904, n_estimators=26, reg_alpha=25.4296608655599, subsample=0.8742398615570541, total= 0.0s [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686 [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686, total= 0.0s [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686 [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686, total= 0.0s [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686 [CV] colsample_bytree=0.8907849255573902, gamma=9.9346046309985, learning_rate=0.38379460560721185, max_depth=36, min_child_weight=125.20972702800735, n_estimators=8, reg_alpha=68.79385255352041, subsample=0.9781684065852686, total= 0.0s [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352 [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352, total= 0.0s [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352 [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352, total= 0.0s [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352 [CV] colsample_bytree=0.929605962100854, gamma=2.5689351891454337, learning_rate=0.0882526543052029, max_depth=3, min_child_weight=15.253308260614421, n_estimators=29, reg_alpha=19.901743299224854, subsample=0.9808827113163352, total= 0.0s [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525 [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525, total= 0.0s [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525 [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525, total= 0.0s [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525 [CV] colsample_bytree=0.7792425647722292, gamma=2.368687035569985, learning_rate=0.2044016044090382, max_depth=8, min_child_weight=37.306512981118345, n_estimators=12, reg_alpha=23.297831295710196, subsample=0.8954194786743525, total= 0.0s [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354 [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354, total= 0.0s [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354 [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354, total= 0.0s [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354 [CV] colsample_bytree=0.9213491831089688, gamma=2.4979053418638895, learning_rate=0.35112873408705864, max_depth=13, min_child_weight=141.57781671037563, n_estimators=21, reg_alpha=74.46671546116669, subsample=0.6631160615502354, total= 0.0s [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446 [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446, total= 0.0s [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446 [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446, total= 0.0s [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446 [CV] colsample_bytree=0.9813081076844272, gamma=0.41475079576498874, learning_rate=0.17873151998809783, max_depth=3, min_child_weight=59.18488883891633, n_estimators=26, reg_alpha=14.464165778976199, subsample=0.9855798461133446, total= 0.0s [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731 [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731, total= 0.0s [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731 [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731, total= 0.0s [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731 [CV] colsample_bytree=0.9319809869450985, gamma=7.641630474650334, learning_rate=0.3763295340690517, max_depth=35, min_child_weight=48.046235077153966, n_estimators=27, reg_alpha=94.09530467636145, subsample=0.9980504267938731, total= 0.0s [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551 [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551, total= 0.1s [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551 [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551, total= 0.1s [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551 [CV] colsample_bytree=0.7810472404244629, gamma=0.509123835168872, learning_rate=0.14402874563640583, max_depth=38, min_child_weight=3.3287041397476425, n_estimators=37, reg_alpha=17.081316004836246, subsample=0.9601700712015551, total= 0.1s [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637 [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637, total= 0.0s [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637 [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637, total= 0.0s [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637 [CV] colsample_bytree=0.9553548432244114, gamma=3.5496191113748434, learning_rate=0.21091093108795217, max_depth=38, min_child_weight=188.81084143186024, n_estimators=3, reg_alpha=61.14438395631305, subsample=0.9560652404397637, total= 0.0s [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718 [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718, total= 0.0s [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718 [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718, total= 0.0s [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718 [CV] colsample_bytree=0.8480014254137175, gamma=6.096583426365768, learning_rate=0.24870107988230855, max_depth=28, min_child_weight=14.613900380015398, n_estimators=39, reg_alpha=84.26715450016715, subsample=0.9888901564054718, total= 0.0s [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255 [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255, total= 0.0s [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255 [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255, total= 0.0s [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255 [CV] colsample_bytree=0.9744058218541515, gamma=2.9507857524949586, learning_rate=0.3990744659677848, max_depth=23, min_child_weight=3.329979147595724, n_estimators=19, reg_alpha=4.127239852707413, subsample=0.9318278660909255, total= 0.0s [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156 [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156, total= 0.0s [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156 [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156, total= 0.0s [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156 [CV] colsample_bytree=0.9925474376498676, gamma=5.115972970056662, learning_rate=0.12378024542969297, max_depth=32, min_child_weight=107.60298089179803, n_estimators=13, reg_alpha=19.001725628050046, subsample=0.9079973453605156, total= 0.0s [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969 [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969, total= 0.0s [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969 [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969, total= 0.0s [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969 [CV] colsample_bytree=0.9470955032103993, gamma=8.472394764586063, learning_rate=0.3956872015105697, max_depth=12, min_child_weight=49.097485232345235, n_estimators=19, reg_alpha=45.68785832483281, subsample=0.9943005201213969, total= 0.0s [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873 [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873, total= 0.0s [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873 [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873, total= 0.0s [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873 [CV] colsample_bytree=0.9987744424423294, gamma=3.2825610365078086, learning_rate=0.4170425390577573, max_depth=12, min_child_weight=52.553403831939406, n_estimators=15, reg_alpha=46.40198056641713, subsample=0.8276000392473873, total= 0.0s [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819 [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819, total= 0.0s [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819 [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819, total= 0.0s [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819 [CV] colsample_bytree=0.9279049918335398, gamma=1.267357091420086, learning_rate=0.2859294592439557, max_depth=36, min_child_weight=11.168561137155821, n_estimators=18, reg_alpha=53.49130997158936, subsample=0.8160414295335819, total= 0.0s [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066 [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066, total= 0.0s [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066 [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066, total= 0.0s [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066 [CV] colsample_bytree=0.9217589706442475, gamma=2.0211656769454045, learning_rate=0.39132388667670553, max_depth=23, min_child_weight=44.321005016727135, n_estimators=32, reg_alpha=33.20029808377724, subsample=0.8468330105596066, total= 0.0s [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448 [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448, total= 0.0s [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448 [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448, total= 0.0s [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448 [CV] colsample_bytree=0.9370726723194647, gamma=1.2890024298161562, learning_rate=0.17351709628123335, max_depth=6, min_child_weight=32.85969411653827, n_estimators=16, reg_alpha=55.80918587730207, subsample=0.8467496210296448, total= 0.0s [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094 [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094, total= 0.0s [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094 [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094, total= 0.1s [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094 [CV] colsample_bytree=0.9882326458866737, gamma=7.60802456305756, learning_rate=0.3624183288775828, max_depth=28, min_child_weight=52.11635668562104, n_estimators=38, reg_alpha=47.56023651231594, subsample=0.9457133991353094, total= 0.1s [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567 [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567, total= 0.0s [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567 [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567, total= 0.0s [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567 [CV] colsample_bytree=0.9956221844796125, gamma=4.255714499580487, learning_rate=0.4331346701783154, max_depth=22, min_child_weight=135.15184636283593, n_estimators=3, reg_alpha=45.769058335664084, subsample=0.9526902894798567, total= 0.0s [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885 [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885, total= 0.0s [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885 [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885, total= 0.0s [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885 [CV] colsample_bytree=0.9764741539362997, gamma=0.254762357788203, learning_rate=0.3417590793024611, max_depth=32, min_child_weight=80.69752871068955, n_estimators=26, reg_alpha=27.96950731543939, subsample=0.892989943330885, total= 0.0s [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377 [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377, total= 0.0s [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377 [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377, total= 0.0s [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377 [CV] colsample_bytree=0.7301234216930994, gamma=3.0499397583521604, learning_rate=0.39837824366442137, max_depth=21, min_child_weight=20.012279875342234, n_estimators=16, reg_alpha=114.74123889704305, subsample=0.8370860677564377, total= 0.0s [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573 [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573, total= 0.0s [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573 [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573, total= 0.0s [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573 [CV] colsample_bytree=0.9628482146099234, gamma=1.51375569992799, learning_rate=0.30414838459538135, max_depth=38, min_child_weight=86.25629386661218, n_estimators=16, reg_alpha=156.5220862826021, subsample=0.9750849728808573, total= 0.0s
[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 9.5s finished
Out[88]:
RandomizedSearchCV(cv=3,
estimator=XGBRegressor(base_score=None, booster=None,
colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, gamma=None,
gpu_id=None, importance_type='gain',
interaction_constraints=None,
learning_rate=None,
max_delta_step=None, max_depth=None,
min_child_weight=None, missing=nan,
monotone_constraints=None,
n_estimators=100, n...
'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000019F745E18B0>,
'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000019F745E1880>,
'reg_alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000019F745E18B0>,
'subsample': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000019F742DFEE0>},
random_state=1, verbose=2)
In [89]:
xgbrs.best_params_
Out[89]:
{'colsample_bytree': 0.8095793735045576,
'gamma': 4.784503456129699,
'learning_rate': 0.20966135876466158,
'max_depth': 25,
'min_child_weight': 12.991263135531565,
'n_estimators': 34,
'reg_alpha': 7.466469039520882,
'subsample': 0.9699481172402621}
In [90]:
xgb_best_random = xgbrs.best_estimator_
xgb_rscv_train = xgb_best_random.score(x_train, y_train.values.ravel())
xgb_rscv_test = xgb_best_random.score(x_test, y_test.values.ravel())
xgb_rscv = cross_validate(xgb_best_random, x, y.values.ravel(), cv=10)
In [91]:
model_comparison['XGBoost Randomized Search CV'] = pd.Series(data=[rf_rscv_train, rf_rscv_test, xgb_rscv['test_score'].mean(), xgb_rscv['test_score'].std()],index=labels)
model_comparison.T
Out[91]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
| Randomized Search CV | 0.983311 | 0.905970 | 0.917427 | 0.024922 |
| Grid Search CV | 0.983307 | 0.905936 | 0.917525 | 0.024961 |
| XGBoost | 0.994335 | 0.916113 | 0.933395 | 0.027554 |
| XGBoost Randomized Search CV | 0.983311 | 0.905970 | 0.929174 | 0.019948 |
In [92]:
xgb_grid_base = XGBRegressor(random_state=1)
In [93]:
grid_params = {
'colsample_bytree': [.86,.87,.88],
'gamma': [7.5,7.6,7.7],
'learning_rate': [.1,.15,.2,.25,.3],
'max_depth': [13],
'min_child_weight': [5.6,5.7,5.8],
'n_estimators': [37],
'reg_alpha': [23,23.5,24,24.5],
'subsample': [.8,.9,1]
}
xgb_gscv = GridSearchCV(estimator=xgb_grid_base, param_grid=grid_params, cv=3, n_jobs=-1, verbose=2)
In [94]:
xgb_gscv.fit(x_train, y_train.values.ravel())
Fitting 3 folds for each of 1620 candidates, totalling 4860 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 2.7s [Parallel(n_jobs=-1)]: Done 468 tasks | elapsed: 10.8s [Parallel(n_jobs=-1)]: Done 1280 tasks | elapsed: 24.4s [Parallel(n_jobs=-1)]: Done 2412 tasks | elapsed: 42.7s [Parallel(n_jobs=-1)]: Done 3872 tasks | elapsed: 1.1min [Parallel(n_jobs=-1)]: Done 4860 out of 4860 | elapsed: 1.4min finished
Out[94]:
GridSearchCV(cv=3,
estimator=XGBRegressor(base_score=None, booster=None,
colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, gamma=None,
gpu_id=None, importance_type='gain',
interaction_constraints=None,
learning_rate=None, max_delta_step=None,
max_depth=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=...
scale_pos_weight=None, subsample=None,
tree_method=None, validate_parameters=None,
verbosity=None),
n_jobs=-1,
param_grid={'colsample_bytree': [0.86, 0.87, 0.88],
'gamma': [7.5, 7.6, 7.7],
'learning_rate': [0.1, 0.15, 0.2, 0.25, 0.3],
'max_depth': [13], 'min_child_weight': [5.6, 5.7, 5.8],
'n_estimators': [37],
'reg_alpha': [23, 23.5, 24, 24.5],
'subsample': [0.8, 0.9, 1]},
verbose=2)
In [95]:
xgbgs_best = xgb_gscv.best_estimator_
xgb_gscv_train = xgbgs_best.score(x_train, y_train.values.ravel())
xgb_gscv_test = xgbgs_best.score(x_test, y_test.values.ravel())
xgbgs_cv = cross_validate(xgbgs_best, x, y.values.ravel(), cv=10)
In [96]:
model_comparison['XGBoost Grid Search CV'] = pd.Series(data=[rf_gscv_train, rf_gscv_test, xgbgs_cv['test_score'].mean(), xgbgs_cv['test_score'].std()],
index=labels)
model_comparison.T
Out[96]:
| Training Score | Testing Score | 10-Fold CV Mean | 10-Fold CV Std. Dev. | |
|---|---|---|---|---|
| Simple Linear Regression | 0.645278 | 0.648877 | 0.622869 | 0.076005 |
| Decision Tree | 0.994855 | 0.846844 | 0.861023 | 0.048994 |
| Random Forest | 0.982552 | 0.909249 | 0.919497 | 0.024503 |
| Random Forest with MinMaxScaler | 0.982306 | 0.906661 | 0.920405 | 0.023778 |
| Gradient Boosting Regressor | 0.952045 | 0.899542 | 0.904101 | 0.022228 |
| Randomized Search CV | 0.983311 | 0.905970 | 0.917427 | 0.024922 |
| Grid Search CV | 0.983307 | 0.905936 | 0.917525 | 0.024961 |
| XGBoost | 0.994335 | 0.916113 | 0.933395 | 0.027554 |
| XGBoost Randomized Search CV | 0.983311 | 0.905970 | 0.929174 | 0.019948 |
| XGBoost Grid Search CV | 0.983307 | 0.905936 | 0.922162 | 0.023979 |